Addressing Social Bias in Information Retrieval
By Jahna Otterbacher. 2018
12 citations recorded by the source corpus.
Journalists and researchers alike have claimed that IR systems are socially biased, returning results to users that perpetuate gender and racial stereotypes. In this position paper, I argue that IR researchers and in particular, evaluation communities such as CLEF, can and should address such concerns. Using as a guide the Principles for Algorithmic Transparency and Accountability recently put forward by the Association for Computing Machinery, I provide examples of techniques for examining social biases in IR systems and in particular, search engines.
Subfields: Safety, Robustness & Interpretability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Safety, Robustness & Interpretability
- Ruslan Salakhutdinov
- Percy Liang
- Chiyu Wu
- Louis-philippe Morency
- Mohit Bansal
- Eric Wallace
- Pang Wei Koh
- Nicholas Carlini
Citations in the corpus
- Towards Understanding and Mitigating Social Biases in Language Models (2021, background): “These approaches have also been applied to image captioning (Hendricks et al., 2018), image retrieval (Otterbacher, 2018), and dialog (Liu et al., 2020).”